Citizen data scientists

Tom Breur

22 July 2018

Gartner introduced the term “citizen data scientists” to refer to non-technical staff who can perform their duties with little or no support from IT. Many of them exclusively resort to point-and-click GUI-based tools, either because that is their comfort zone, or because using a programming language would require IT support. Think for instance about installing drivers, necessary libraries, etc.

The push for “self-service BI” is making analytics pervasive throughout the organization. This growth was triggered in part because there is a genuine need for “smarter” information products <ref: Smart Enough Systems>, combined with a new generation that enters the workforce more computer savvy and data-aware than we have ever seen before. Our children grew up with computers and have always had tools and abundant data available. Small wonder that is what they (also) expect on the job.

When I look at job descriptions for data scientists, often written for these so-called citizen data scientists, I get the impression that the unglamorous (but crucial!) work of extracting, cleaning, transforming and wrangling of their data sources actually is the only thing data scientists do. The harsh reality is that many of us have access to plenty of data, but most sources don’t come in a form that is (immediately) amenable to analysis. As John Naisbitt has said: “We’re drowning in information but starving for Knowledge.”

To me, hiring so-called data scientists for ad hoc information extraction seems an understandable, yet short-sighted solution to this problem. Information is data in context, and unless you can relate data points to each other (across the organization), you won’t get value from your data assets. But data are fickle stuff, and rarely do they allow straightforward interpretation.

In the era of “Big Data”, information governance and data architecture will be differentiating capabilities. The rise of CIO’s and CDO’s further underscores this point. The onus is on senior managers, imho, to elevate the contribution of these “citizen data scientists” beyond the menial stitching together and cleansing of data sources. But how much knowledge is required to get there? I find that companies tend to underestimate that journey, and overstate their own capabilities. And as Alice in Wonderland said: if you don’t know where you’re going, then any road will take you there…



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